Increased efforts in healthcare programs over the last few decades have improved the quality of human life. Cancer however, remains a leading cause of death and is considered one of the emerging ‘life-style’ syndromes. Unfortunately the remarkable advancements in molecular elucidation of cancer do not always translate into reliable therapeutic strategies. Radiation, chemo-, hormonal and immune therapies initially often appear to be effective in primary treatment, yet drug refractory and /or recurrent disease arising from persistent residual micrometastases often ensue and lead to adverse patient prognosis. Several unresolved issues such as late detection, failure to recognize the cellular and molecular heterogeneity of tumors, drug resistance, limitations in specific tumor cell targeting, immune evasion by tumor cells, etc. clearly necessitate development of new approaches to improve the efficacy of cancer therapy. Classically the multi-step process of mutation selection and fixing at the cellular level is established as a causative event in transformation. Activation of oncogenes through gain-of-function mutations complemented by inactivation of tumor suppressor genes by loss-of-function mutations provides a strong argument that their cognate proto-oncogenes and tumor suppressor genes normally balance positive and negative regulation of the cell cycle respectively (Coschi & Dick, 2012). Genetic insults can disrupt the cell cycle and lead to unbalanced cell proliferation; the direct correlation between increased mutational load and tumor grade has assigned a significant role for genetic instability in disease progression. Within transformed tissues, the emergence of such events is often not uniform and can generate various lineages. “Intratumor heterogeneity” was first realized by histopathologists as variations in morphology or staining behavior in tumors. At present it refers to the coexistence of derivatives of a transformed cell (suggestive of evolving lineages and differential regenerative capabilities), tumor-associated myoepithelial, inflammatory, immunomodulatory, endothelial, vascular and stromal components within a single tumor. At the molecular level, intratumor heterogeneity encompasses differential gene and protein regulatory networks that define cellular functions and programs.
Detection of heterogeneous behaviour can be carried out through karyotyping, spectral imaging, immunohistochemistry (IHC) based mitotic counts and total nuclear DNA content analysis (ploidy) that identify genetic instability within tumors. While H&E staining and IHC are relatively quick, inexpensive and easy techniques that have remained almost unchanged over several decades, an “all-or-none” type of analysis is impossible for most markers due to considerable intratumor heterogeneity with regard to cell compositions and levels of expression. Despite the availability of automation, manual counting is often considered more reliable, that generates possibilities of subjectivity and discordance in analysis.
Flow cytometry supports IHC through quantification of cells within a population expressing a specific marker rather than its level of expression (achieved in IHC). Such quantification is based on the assumption that the amount of fluorescent dye linearly represents the amount of marker. Profiling specific markers using single- or multi-color fluorophores is thus a well established application of flow cytometry (EP798386B1; EP2472264A3; EP741798A1). Quantification through flow cytometry of cells in S-cell cycle phase by thymidine labelling based DNA content determination is further reported to exhibit good correlation with proliferation index in histologic assessments (Cavanagh et.al. 2011). However since tumor cells also reside in other cell cycle phases, two samples with comparable S-phase fractions may exhibit different growth kinetics and responses to cycle-dependent chemotherapeutic agents. Combinations of marker profiling with cell cycle analysis and/or DNA content are also established (Corver W E et al. 1996). Thus, determination of DNA content along with specific cell cycle phase markers such as Ki-67, PCNA and/or α DNA polymerase increases the accuracy of such analysis (Tanaka et. al. 2011, Liu et.al. 2010, Crevel et.al 2012). The G0 phase however remains elusive due to lack of association with any exclusive marker; although differential DNA-RNA binding can be used to resolve this resting phase (Holyoake et al. 1999).
Application of flow cytometry using a defined panel of cell surface markers to identify cellular components of regenerative hierarchies is also robustly established for the hematopoietic system and is a routine clinical practice that supports therapeutic interventions in aberrant hematopoiesis-associated syndromes including leukaemia (Lapidot et al. 1994). Similar applications in other normal vs. aberrantly functioning adult tissues however, are not so widely established although the identification and prospective isolation of CSCs using flow cytometry or magnetic bead based sorting and application of specific markers expressed on normal stem cells has been frequently reported in the last decade (Al-Hajj et al. 2003, Singh et al. 2004, Collins et al. 2005, Li et al. 2009, Barteneva et al. Biochimica et Biophysica Acta 2013; U.S. Pat. No. 7,115,360; WO2012031280A2; US20080187938; US20080261244; U.S. Pat. Nos. 7,723,112; 8,044,259; 8,110,366). The CSC population is quite likely to be heterogeneous, as is derived from numerous reports of permutations and combinations of different markers or alternative stem cell-like functionalities such as long-term regeneration potential (Bapat et al. 2005; Smith et. al. 2011; US20130157285A1) or side-population efflux (Zhao et.al 2013) to isolate tumor fractions with comparable performances in regenerative assays. Hence, the initial expectations of possibilities from CSC identification relating to prospective isolation, characterization and investigation of crucial biological functionalities of these cells, have not really been achieved.
Marker-free identification of normal tissue stem cells and studies of their proliferation kinetics using label-chase/label quenching are earlier reported to be useful especially in tissues wherein precise marker association cannot be assigned and confirmed (Lanzkron et.al., 1999; Rousselle et al. 2001; Boutonnat et.al 2005). We had previously applied a label-chase approach to identify tumor dormancy wherein resolution of the proliferative hierarchy was derived through differential label retention, and that aneuploid cells emerging in a developing tumor contribute to drug refractory behavior (Kusumbe and Bapat, 2009a). This was the first report of application of label-chase in tumors; use of the same for CSC isolation of is in the public domain (http://www.sigmaaldrich.com/technical-documents/articles/biowire/cell-tracking-lipophilic-membrane-dyes.html) and has been used thereafter (Rainusso et. al., 2011; Ramachandran et. al., 2011; Du et. al., 2012; Ricci et. al., 2012; Wang et. al., 2012; Xue et. al., 2012; Morrison et. al. 2013; Richichi et. al., 2013). Although the study established an association of CSCs and aneuploidy with tumor dormancy, it remained a subjective observation of tumor behavior, with each parameter being studied in isolation that overlooked possible cross-regulation. Most importantly, the true tumor heterogeneity in terms of interdependent populations was not quantified. These limitations led the present inventors to develop the present invention in a non-obvious manner that is useful in understanding tumor behavior, and resulted in an improved method of identification and quantification of tumour heterogeneity.
The present invention thus describes a novel method which is based on a structured population model achieved by deconstruction of tumors into discrete cell fractions mapped through concurrent analysis of regenerative hierarchies, genetic alterations and cell cycle effects in a non-obvious manner. This is a major advancement over any individual technique and achieves a higher and directed resolution of different yet relevant tumor cell types in a quantifiable manner.